The need for abstraction in phonology: A commentary on Ambridge (2020)

2020 ◽  
Vol 40 (5-6) ◽  
pp. 576-580 ◽  
Author(s):  
Sara Finley
Keyword(s):  
Optimality Theory ◽  
Generative Models ◽  
Levels Of Abstraction ◽  
Exemplar Models ◽  
Abstract Representations ◽  
Multiple Levels

In this commentary, I discuss why, despite the existence of gradience in phonetics and phonology, there is still a need for abstract representations. Most proponents of exemplar models assume multiple levels of abstraction, allowing for an integration of the gradient and the categorical. Ben Ambridge’s dismissal of generative models such as Optimality Theory (OT) is problematic because OT not only allows for the abstract, but can also handle a variety of phenomena, including gradient representations, and similarity among output forms.

Scale-free architectures support representational diversity

2020 ◽  
Vol 43 ◽  
Author(s):  
Chris Fields ◽  
James F. Glazebrook
Keyword(s):  
Cognitive Architecture ◽  
Levels Of Abstraction ◽  
Scale Free ◽  
Abstract Representations ◽  
Computational Architecture ◽  
Architectural Models ◽  
Cognition Scale

Abstract Gilead et al. propose an ontology of abstract representations based on folk-psychological conceptions of cognitive architecture. There is, however, no evidence that the experience of cognition reveals the architecture of cognition. Scale-free architectural models propose that cognition has the same computational architecture from sub-cellular to whole-organism scales. This scale-free architecture supports representations with diverse functions and levels of abstraction.

Learning at Multiple Levels of Abstraction: The Case of Verb Argument Constructions

2007 ◽  
Author(s):  
Amy Perfors ◽  
Charles Kemp ◽  
Elizabeth Wonnacott ◽  
Joshua B. Tenenbaum
Keyword(s):  
Levels Of Abstraction ◽  
Multiple Levels

scholarly journals A Model for Analysing the Collective Dynamic Behaviour and Characterising the Exploitation of Population-Based Algorithms

2014 ◽  
Vol 22 (1) ◽  
pp. 159-188 ◽  
Author(s):  
Mikdam Turkey ◽  
Riccardo Poli
Keyword(s):  
Population Dynamics ◽  
Dynamic Behaviour ◽  
Fitness Landscape ◽  
Population Based ◽  
Levels Of Abstraction ◽  
Collective Dynamic ◽  
Model Studies ◽  
Proposed Model ◽  
Wide Range ◽  
Multiple Levels

Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify features associated with it. The proposed model studies the topological distribution of an algorithm's activity from both a genotypic and a phenotypic perspective, and represents population dynamics using multiple levels of abstraction. The model can have different instantiations. Here it has been implemented using a modified version of self-organising maps. These are used to represent and track the population motion in the fitness landscape as the algorithm operates on solving a problem. Based on this model, we developed a set of features that characterise the population's collective dynamic behaviour. By analysing them and revealing their dependency on fitness distributions, we were then able to define an indicator of the exploitation behaviour of an algorithm. This is an entropy-based measure that assesses the dependency on fitness distributions of different features of population dynamics. To test the proposed measures, evolutionary algorithms with different crossover operators, selection pressure levels and population handling techniques have been examined, which lead populations to exhibit a wide range of exploitation-exploration behaviours.

Abstractions are good for brains and machines: A commentary on Ambridge (2020)

2020 ◽  
Vol 40 (5-6) ◽  
pp. 631-635
Author(s):  
Kathryn D. Schuler ◽  
Jordan Kodner ◽  
Spencer Caplan
Keyword(s):  
Computer Science ◽  
Exemplar Models ◽  
Abstract Representations ◽  
Fundamental Part ◽  
Good For

In ‘Against Stored Abstractions,’ Ambridge uses neural and computational evidence to make his case against abstract representations. He argues that storing only exemplars is more parsimonious – why bother with abstraction when exemplar models with on-the-fly calculation can do everything abstracting models can and more – and implies that his view is well supported by neuroscience and computer science. We argue that there is substantial neural, experimental, and computational evidence to the contrary: while both brains and machines can store exemplars, forming categories and storing abstractions is a fundamental part of what they do.

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